Mechanical-Ventilation Supply and Options for the COVID-19 Pandemic. Leveraging All Available Resources for a Limited Resource in a Crisis

Non-invasive ventilation versus mechanical ventilation in hypoxemic patients with COVID-19

Purpose

Limited mechanical ventilators (MV) during the Coronavirus disease (COVID-19) pandemic have led to the use of non-invasive ventilation (NIV) in hypoxemic patients, which has not been studied well. We aimed to assess the association of NIV versus MV with mortality and morbidity during respiratory intervention among hypoxemic patients admitted with COVID-19.

Methods

We performed a retrospective multi-center cohort study across 5 hospitals during March–April 2020. Outcomes included mortality, severe COVID-19-related symptoms, time to discharge, and final oxygen saturation (SpO2) at the conclusion of the respiratory intervention. Multivariable regression of outcomes was conducted in all hypoxemic participants, 4 subgroups, and propensity-matched analysis.

Results

Of 2381 participants with laboratory-confirmed SARS-CoV-2, 688 were included in the study who were hypoxemic upon initiation of respiratory intervention. During the study period, 299 participants died (43%), 163 were admitted to the ICU (24%), and 121 experienced severe COVID-19-related symptoms (18%). Participants on MV had increased mortality than those on NIV (128/154 [83%] versus 171/534 [32%], OR = 30, 95% CI 16–60) with a mean survival of 6 versus 15 days, respectively. The MV group experienced more severe COVID-19-related symptoms [55/154 (36%) versus 66/534 (12%), OR = 4.3, 95% CI 2.7–6.8], longer time to discharge (mean 17 versus 7.1 days), and lower final SpO2 (92 versus 94%). Across all subgroups and propensity-matched analysis, MV was associated with a greater OR of death than NIV.

Conclusions

NIV was associated with lower respiratory intervention mortality and morbidity than MV. However, findings may be liable to unmeasured confounding and further study from randomized controlled trials is needed to definitively determine the role of NIV in hypoxemic patients with COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-021-01633-6.

Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.

Bed Surge Capacity in Saudi Hospitals During the COVID-19 Pandemic

OBJECTIVES

To assess the hospital beds and intensive care unit (ICU) beds with a ventilator surge capacity of the health system in Kingdom of Saudi Arabia (KSA) during the coronavirus disease (COVID-19) pandemic.

METHODS

This study used relevant data from the National Health Emergency Operation Center to estimate general hospital and ICU bed surge capacity and tipping points under 3 distinct transmission scenarios.

RESULTS

The study results reveal that hospitals in the KSA need to be supplied with additional 4372 hospital beds to care for COVID-19 positive cases if the pandemic continues over a 6 months' period. At the same time, it requires additional 2192 or 1461 hospital beds if the pandemic persists over a 12- or 18-month period, respectively, to manage hospitalized COVID-19 overloads. The health system surge capacity would suffer from a shortage of 1600, 797, and 540 ICU beds under the 3 transmission scenarios to absorb critical and intensive care COVID-19 cases.

CONCLUSION

Our findings highlight the urgent need for additional hospital and ICU beds in the face of critical COVID-19 cases in KSA. The study recommends further assessment measures to the health system surge capacity to keep the Saudi health system prepared during the COVID-19 pandemic.

SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO2 Waveform Prediction.. [PMID: 33688661 PMCID: PMC7941630 DOI: 10.1101/2021.02.25.21252234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO₂Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO₂) waveforms 5 and 30 minutes in the future using only prior SpO₂ values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO₂ waveforms with average MSE below .0007. SWIFT provides information on both occurrence and magnitude of potential hypoxemic events 30 minutes in advance, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.